def gfactor(self, other_body):
"""
Computes the g_ij factor between this object and other_body
g_ij = G * mj / d**3
Were G is the gravitational constant, mj is the mass of the other objects and d is the distans.
Parameters
---------_
other_body : Object
Another body object.
"""
# Gravity constant
G = 0.0374038
if self.obj_id != other_body.obj_id:
# Distance beetween objects
dx = self.obj_position[0] - other_body.obj_position[0]
dy = self.obj_position[1] - other_body.obj_position[1]
dist = np_sqrt(dx*dx + dy*dy)
return G*other_body.obj_mass/(dist*dist*dist)
else:
return 0
def plotAll(self, timer, coreCut, transform=True, ignoreContigLengths=False):
"""Plot all contigs over a certain length which are unbinned"""
self.loadData(timer, "((length >= "+str(coreCut)+"))")
if transform:
self.transformCP(timer)
else:
if self.numStoits == 3:
self.transformedCP = self.covProfiles
else:
print "Number of stoits != 3. You need to transform"
self.transformCP(timer)
fig = plt.figure()
ax1 = fig.add_subplot(111, projection='3d')
if ignoreContigLengths:
sc = ax1.scatter(self.transformedCP[:,0],
self.transformedCP[:,1],
self.transformedCP[:,2],
edgecolors='none',
c=self.contigGCs,
cmap=self.colorMapGC,
vmin=0.0,
vmax=1.0,
marker='.',
s=10.
)
else:
sc = ax1.scatter(self.transformedCP[:,0],
self.transformedCP[:,1],
self.transformedCP[:,2],
edgecolors='k',
c=self.contigGCs,
cmap=self.colorMapGC,
vmin=0.0,
vmax=1.0,
marker='.',
s=np_sqrt(self.contigLengths)
)
sc.set_edgecolors = sc.set_facecolors = lambda *args:None # disable depth transparency effect
self.plotStoitNames(ax1)
cbar = plt.colorbar(sc, shrink=0.5)
cbar.ax.tick_params()
cbar.ax.set_title("% GC", size=10)
cbar.set_ticks([0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8])
#import IPython; IPython.embed()
cbar.ax.set_ylim([0.15, 0.85])
mungeCbar(cbar)
try:
plt.show()
plt.close(fig)
except:
print "Error showing image", exc_info()[0]
raise
del fig
def str_to_matrix(_l, _symbols): #_sym_symbols are eg. myMBS.dynamics
if not type(_symbols) == str:
_str_symbols = str(_symbols).replace("[","").replace("(t)","").replace("]","").replace(", t", "")
else:
_str_symbols = _symbols.replace("[","").replace("(t)","").replace("]","").replace(", t", "")
rows = cols = np_sqrt(len(_l))
m = Matrix.zeros(rows,cols)
for i in range(len(_l)):
c = int(i%cols)
r = int(i/cols)
m[r,c] = str_to_dynexpr(_l[i], _str_symbols)
return m
def tmax_pseudo_ellipse_array(phi, theta_x, theta_y):
"""The pseudo-ellipse tilt-torsion polar angle for numpy arrays.
@param phi: The azimuthal tilt-torsion angle.
@type phi: numpy rank-1 float64 array
@param theta_x: The cone opening angle along x.
@type theta_x: float
@param theta_y: The cone opening angle along y.
@type theta_y: float
@return: The array theta max angles for the given phi angle array.
@rtype: numpy rank-1 float64 array
"""
# Zero points.
if theta_x == 0.0:
return 0.0 * phi
elif theta_y == 0.0:
return 0.0 * phi
# Return the maximum angle.
return theta_x * theta_y / np_sqrt((np_cos(phi)*theta_y)**2 + (np_sin(phi)*theta_x)**2)
def project(self, w):
"""Linearly project the RTs down to a line using direction w.
The projected variances are replaced by the projected standard deviations.
Return the projected statistics.
Input:
w: the projecting direction.
Output:
B: the Stats2e obtained from projecting the RTs.
"""
w = w.ravel()
B0 = self.get_stat(0).reshape(self.J, 1)
M1 = self.get_stat(1).reshape(self.J, self.d)
B1 = dot(M1, w).reshape(self.J, 1)
M2 = self.get_stat(2).reshape(self, J, self.d, self.d)
B2 = np_sqrt(dot(dot(M2, w), w)).reshape(self.J, 1) # this is actually sqrt(w^T Cov w) for each class
BB = concatenate((B0, B1, B2), 1)
return Stats2e(self.J, (1,), BB)
def liouvillian_normal_form(L, symbolic=False):
r"""
Return a Hamilton operator ``H`` and a minimal list of collapse operators ``Ls`` that generate the liouvillian ``L``.
A Liouvillian defined by a hermitian Hamilton operator :math:`H` and a vector of collapse operators
:math:`\mathbf{L} = (L_1, L_2, \dots L_n)^T` is invariant under the following two operations:
.. math::
\left(H, \mathbf{L}\right) & \mapsto \left(H + {1\over 2i}\left(\mathbf{w}^\dagger \mathbf{L} - \mathbf{L}^\dagger \mathbf{w}\right), \mathbf{L} + \mathbf{w} \right) \\
\left(H, \mathbf{L}\right) & \mapsto \left(H, \mathbf{U}\mathbf{L}\right)\\
where :math:`\mathbf{w}` is just a vector of complex numbers and :math:`\mathbf{U}` is a complex unitary matrix.
It turns out that for quantum optical circuit models the set of collapse operators is often linearly dependent.
This routine tries to find a representation of the Liouvillian in terms of a Hamilton operator ``H`` with
as few non-zero collapse operators ``Ls`` as possible.
Consider the following example, which results from a two-port linear cavity with a coherent input into the first port:
>>> kappa_1, kappa_2 = symbols('kappa_1, kappa_2', positive = True)
>>> Delta = symbols('Delta', real = True)
>>> alpha = symbols('alpha')
>>> H = Delta * Create(1) * Destroy(1) + (sqrt(kappa_1) / (2 * I)) * (alpha * Create(1) - alpha.conjugate() * Destroy(1))
>>> Ls = [sqrt(kappa_1) * Destroy(1) + alpha, sqrt(kappa_2) * Destroy(1)]
>>> LL = liouvillian(H, Ls)
>>> Hnf, Lsnf = liouvillian_normal_form(LL)
>>> Hnf
Delta * Create(1) * Destroy(1) - I * sqrt(kappa_1) * (alpha * Create(1) - alpha.conjugate() * Destroy(1))
>>> Lsnf
[sqrt(kappa_1 + kappa_2) * Destroy(1)]
In terms of the ensemble dynamics this final system is equivalent.
Note that this function will only work for proper Liouvillians.
:param L: The Liouvillian
:type L: SuperOperator
:return: ``(H, Ls)``
:rtype: tuple
:raises: BadLiouvillianError
"""
L = L.expand()
if isinstance(L, SuperOperatorPlus):
Ls = []
spres = []
sposts = []
collapse_form = defaultdict(lambda: defaultdict(int))
for s in L.operands:
coeff, term = s.operands if isinstance(s, ScalarTimesSuperOperator) else (sympyOne, s)
if isinstance(term, SPre):
spres.append(coeff * term.operands[0])
elif isinstance(term, SPost):
sposts.append((coeff * term.operands[0]))
else:
if (
not isinstance(term, SuperOperatorTimes)
or not len(term.operands) == 2
or not (isinstance(term.operands[0], SPre) and isinstance(term.operands[1], SPost))
):
raise BadLiouvillianError(
"All terms of the Liouvillian need to be of form "
"SPre(X), SPost(X) or SPre(X)*SPost(X): This term is in violation {!s}".format(term)
)
spreL, spostL = term.operands
Li, Ljd = spreL.operands[0], spostL.operands[0]
try:
complex(coeff)
except ValueError:
symbolic = True
coeff = coeff.simplify()
collapse_form[Li][Ljd] = coeff
basis = sorted(collapse_form.keys())
for ii, Li in enumerate(basis):
for Lj in basis[ii:]:
cij = collapse_form[Li][Lj.adjoint()]
cji = collapse_form[Lj][Li.adjoint()]
if cij != 0 or cji != 0:
diff = cij.conjugate() - cji
try:
diff = complex(diff)
if abs(diff) > 1e-6:
print(
(
"Warning: the Liouvillian is probably malformed: "
"The coefficients of SPre({!s})*SPost({!s}) and SPre({!s})*SPost({!s}) "
"should be complex conjugates of each other"
).format(Li, Lj.adjoint(), Lj, Li.adjoint())
)
except ValueError:
symbolic = True
if diff.simplify():
print("Warning: the Liouvillian my be malformed, convert to numerical representation")
final_Lis = []
if symbolic:
if len(basis) == 1:
l1 = basis[0]
kappa1 = collapse_form[l1][l1.adjoint()]
final_Lis = [sqrt(kappa1) * l1]
sdiff = l1.adjoint() * l1 * kappa1 / 2
spres.append(sdiff)
sposts.append(sdiff)
# elif len(basis) == 2:
# l1, l2 = basis
# kappa_1 = collapse_form[l1][l1.adjoint()]
# kappa_2 = collapse_form[l2][l2.adjoint()]
# kappa_12 = collapse_form[l1][l2.adjoint()]
# kappa_21 = collapse_form[l2][l1.adjoint()]
## assert (kappa_12.conjugate() - kappa_21) == 0
else:
M = SympyMatrix(len(basis), len(basis), lambda i, j: collapse_form[basis[i]][basis[j].adjoint()])
# First check if M is already diagonal (sympy does not handle this well, for some reason)
diag = True
for i in range(len(basis)):
for j in range(i):
if M[i, j].simplify() != 0 or M[j, i].simplify != 0:
diag = False
break
if diag == False:
break
if diag:
for bj in basis:
final_Lis.append(bj * sqrt(collapse_form[bj][bj.adjoint()]))
sdiff = bj.adjoint() * bj * collapse_form[bj][bj.adjoint()] / 2
spres.append(sdiff)
sposts.append(sdiff)
# Try sympy algo
else:
try:
data = M.eigenvects()
for evalue, multiplicity, ebasis in data:
if not evalue:
continue
for b in ebasis:
new_L = (sqrt(evalue) * sum(cj[0] * Lj for (cj, Lj) in zip(b.tolist(), basis))).expand()
final_Lis.append(new_L)
sdiff = (new_L.adjoint() * new_L / 2).expand()
spres.append(sdiff)
sposts.append(sdiff)
except NotImplementedError:
raise CannotSymbolicallyDiagonalize(
(
"The matrix {} is too hard to diagonalize symbolically. "
"Please try converting to fully numerical representation."
).format(M)
)
else:
M = np_array([[complex(collapse_form[Li][Lj.adjoint()]) for Lj in basis] for Li in basis])
vals, vecs = eigh(M)
for sv, vec in zip(np_sqrt(vals), vecs.transpose()):
new_L = sum((sv * ci) * Li for (ci, Li) in zip(vec, basis))
final_Lis.append(new_L)
sdiff = (0.5 * new_L.adjoint() * new_L).expand()
spres.append(sdiff)
sposts.append(sdiff)
miHspre = sum(spres)
iHspost = sum(sposts)
if not (miHspre + iHspost) is ZeroOperator or not (miHspre.adjoint() + miHspre) is ZeroOperator:
print("Warning, potentially malformed Liouvillian {!s}".format(L))
final_H = (I * miHspre).expand()
return final_H, final_Lis
else:
if L is ZeroSuperOperator:
return ZeroOperator, []
raise BadLiouvillianError(str(L))
def renderTransCPData(self,
fileName="",
show=True,
elev=45,
azim=45,
all=False,
showAxis=False,
primaryWidth=12,
primarySpace=3,
dpi=300,
format='png',
fig=None,
highlight=None,
restrictedBids=[],
alpha=1,
ignoreContigLengths=False):
"""Plot transformed data in 3D"""
del_fig = False
if(fig is None):
fig = plt.figure()
del_fig = True
else:
plt.clf()
if(all):
myAXINFO = {
'x': {'i': 0, 'tickdir': 1, 'juggled': (1, 0, 2),
'color': (0, 0, 0, 0, 0)},
'y': {'i': 1, 'tickdir': 0, 'juggled': (0, 1, 2),
'color': (0, 0, 0, 0, 0)},
'z': {'i': 2, 'tickdir': 0, 'juggled': (0, 2, 1),
'color': (0, 0, 0, 0, 0)},
}
ax = fig.add_subplot(131, projection='3d')
sc = ax.scatter(self.transformedCP[:,0], self.transformedCP[:,1], self.transformedCP[:,2], edgecolors='k', c=self.contigGCs, cmap=self.colorMapGC, vmin=0.0, vmax=1.0, marker='.')
sc.set_edgecolors = sc.set_facecolors = lambda *args:None # disable depth transparency effect
ax.azim = 0
ax.elev = 0
ax.set_xlim3d(0,self.scaleFactor)
ax.set_ylim3d(0,self.scaleFactor)
ax.set_zlim3d(0,self.scaleFactor)
ax.set_xticklabels([])
ax.set_yticklabels([])
ax.set_zticklabels([])
ax.set_xticks([])
ax.set_yticks([])
ax.set_zticks([])
for axis in ax.w_xaxis, ax.w_yaxis, ax.w_zaxis:
for elt in axis.get_ticklines() + axis.get_ticklabels():
elt.set_visible(False)
ax.w_xaxis._AXINFO = myAXINFO
ax.w_yaxis._AXINFO = myAXINFO
ax.w_zaxis._AXINFO = myAXINFO
ax = fig.add_subplot(132, projection='3d')
sc = ax.scatter(self.transformedCP[:,0], self.transformedCP[:,1], self.transformedCP[:,2], edgecolors='k', c=self.contigGCs, cmap=self.colorMapGC, vmin=0.0, vmax=1.0, marker='.')
sc.set_edgecolors = sc.set_facecolors = lambda *args:None # disable depth transparency effect
ax.azim = 90
ax.elev = 0
ax.set_xlim3d(0,self.scaleFactor)
ax.set_ylim3d(0,self.scaleFactor)
ax.set_zlim3d(0,self.scaleFactor)
ax.set_xticklabels([])
ax.set_yticklabels([])
ax.set_zticklabels([])
ax.set_xticks([])
ax.set_yticks([])
ax.set_zticks([])
for axis in ax.w_xaxis, ax.w_yaxis, ax.w_zaxis:
for elt in axis.get_ticklines() + axis.get_ticklabels():
elt.set_visible(False)
ax.w_xaxis._AXINFO = myAXINFO
ax.w_yaxis._AXINFO = myAXINFO
ax.w_zaxis._AXINFO = myAXINFO
ax = fig.add_subplot(133, projection='3d')
sc = ax.scatter(self.transformedCP[:,0], self.transformedCP[:,1], self.transformedCP[:,2], edgecolors='k', c=self.contigGCs, cmap=self.colorMapGC, vmin=0.0, vmax=1.0, marker='.')
sc.set_edgecolors = sc.set_facecolors = lambda *args:None # disable depth transparency effect
ax.azim = 0
ax.elev = 90
ax.set_xlim3d(0,self.scaleFactor)
ax.set_ylim3d(0,self.scaleFactor)
ax.set_zlim3d(0,self.scaleFactor)
ax.set_xticklabels([])
ax.set_yticklabels([])
ax.set_zticklabels([])
ax.set_xticks([])
ax.set_yticks([])
ax.set_zticks([])
for axis in ax.w_xaxis, ax.w_yaxis, ax.w_zaxis:
for elt in axis.get_ticklines() + axis.get_ticklabels():
elt.set_visible(False)
ax.w_xaxis._AXINFO = myAXINFO
ax.w_yaxis._AXINFO = myAXINFO
ax.w_zaxis._AXINFO = myAXINFO
else:
ax = fig.add_subplot(111, projection='3d')
if len(restrictedBids) == 0:
if highlight is None:
print "BF:", np_shape(self.transformedCP)
if ignoreContigLengths:
sc = ax.scatter(self.transformedCP[:,0],
self.transformedCP[:,1],
self.transformedCP[:,2],
edgecolors='none',
c=self.contigGCs,
cmap=self.colorMapGC,
s=10.,
vmin=0.0,
vmax=1.0,
marker='.')
else:
sc = ax.scatter(self.transformedCP[:,0],
self.transformedCP[:,1],
self.transformedCP[:,2],
edgecolors='none',
c=self.contigGCs,
cmap=self.colorMapGC,
vmin=0.0,
vmax=1.0,
s=np_sqrt(self.contigLengths),
marker='.')
sc.set_edgecolors = sc.set_facecolors = lambda *args:None # disable depth transparency effect
else:
#draw the opaque guys first
"""
sc = ax.scatter(self.transformedCP[:,0],
self.transformedCP[:,1],
self.transformedCP[:,2],
edgecolors='none',
c=self.contigGCs,
cmap=self.colorMapGC,
vmin=0.0,
vmax=1.0,
s=100.,
marker='s',
alpha=alpha)
sc.set_edgecolors = sc.set_facecolors = lambda *args:None # disable depth transparency effect
"""
# now replot the highlighted guys
disp_vals = np_array([])
disp_GCs = np_array([])
thrower = {}
hide_vals = np_array([])
hide_GCs = np_array([])
num_points = 0
for bin in highlight:
for row_index in bin.rowIndices:
num_points += 1
disp_vals = np_append(disp_vals, self.transformedCP[row_index])
disp_GCs = np_append(disp_GCs, self.contigGCs[row_index])
thrower[row_index] = False
# reshape
disp_vals = np_reshape(disp_vals, (num_points, 3))
num_points = 0
for i in range(len(self.indices)):
try:
thrower[i]
except KeyError:
num_points += 1
hide_vals = np_append(hide_vals, self.transformedCP[i])
hide_GCs = np_append(hide_GCs, self.contigGCs[i])
# reshape
hide_vals = np_reshape(hide_vals, (num_points, 3))
sc = ax.scatter(hide_vals[:,0],
hide_vals[:,1],
hide_vals[:,2],
edgecolors='none',
c=hide_GCs,
cmap=self.colorMapGC,
vmin=0.0,
vmax=1.0,
s=100.,
marker='s',
alpha=alpha)
sc.set_edgecolors = sc.set_facecolors = lambda *args:None # disable depth transparency effect
sc = ax.scatter(disp_vals[:,0],
disp_vals[:,1],
disp_vals[:,2],
edgecolors='none',
c=disp_GCs,
cmap=self.colorMapGC,
vmin=0.0,
vmax=1.0,
s=10.,
marker='.')
sc.set_edgecolors = sc.set_facecolors = lambda *args:None # disable depth transparency effect
print np_shape(disp_vals), np_shape(hide_vals), np_shape(self.transformedCP)
# render color bar
cbar = plt.colorbar(sc, shrink=0.5)
cbar.ax.tick_params()
cbar.ax.set_title("% GC", size=10)
cbar.set_ticks([0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8])
cbar.ax.set_ylim([0.15, 0.85])
mungeCbar(cbar)
else:
r_trans = np_array([])
r_cols=np_array([])
num_added = 0
for i in range(len(self.indices)):
if self.binIds[i] not in restrictedBids:
r_trans = np_append(r_trans, self.transformedCP[i])
r_cols = np_append(r_cols, self.contigGCs[i])
num_added += 1
r_trans = np_reshape(r_trans, (num_added,3))
print np_shape(r_trans)
#r_cols = np_reshape(r_cols, (num_added,3))
sc = ax.scatter(r_trans[:,0],
r_trans[:,1],
r_trans[:,2],
edgecolors='none',
c=r_cols,
cmap=self.colorMapGC,
s=10.,
vmin=0.0,
vmax=1.0,
marker='.')
sc.set_edgecolors = sc.set_facecolors = lambda *args:None # disable depth transparency effect
# render color bar
cbar = plt.colorbar(sc, shrink=0.5)
cbar.ax.tick_params()
cbar.ax.set_title("% GC", size=10)
cbar.set_ticks([0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8])
cbar.ax.set_ylim([0.15, 0.85])
mungeCbar(cbar)
ax.azim = azim
ax.elev = elev
ax.set_xlim3d(0,self.scaleFactor)
ax.set_ylim3d(0,self.scaleFactor)
ax.set_zlim3d(0,self.scaleFactor)
ax.set_xticklabels([])
ax.set_yticklabels([])
ax.set_zticklabels([])
ax.set_xticks([])
ax.set_yticks([])
ax.set_zticks([])
if(not showAxis):
ax.set_axis_off()
if(fileName != ""):
try:
if(all):
fig.set_size_inches(3*primaryWidth+2*primarySpace,primaryWidth)
else:
fig.set_size_inches(primaryWidth,primaryWidth)
plt.savefig(fileName,dpi=dpi,format=format)
except:
print "Error saving image",fileName, exc_info()[0]
raise
elif(show):
try:
plt.show()
except:
print "Error showing image", exc_info()[0]
raise
if del_fig:
plt.close(fig)
del fig
def plotUnbinned(self, timer, coreCut, transform=True, ignoreContigLengths=False):
"""Plot all contigs over a certain length which are unbinned"""
self.loadData(timer, "((length >= "+str(coreCut)+") & (bid == 0))")
if transform:
self.transformCP(timer)
else:
if self.numStoits == 3:
self.transformedCP = self.covProfiles
else:
print "Number of stoits != 3. You need to transform"
self.transformCP(timer)
fig = plt.figure()
ax1 = fig.add_subplot(111, projection='3d')
if ignoreContigLengths:
sc = ax1.scatter(self.transformedCP[:,0], self.transformedCP[:,1], self.transformedCP[:,2], edgecolors='none', c=self.contigGCs, cmap=self.colorMapGC, vmin=0.0, vmax=1.0, s=10, marker='.')
else:
sc = ax1.scatter(self.transformedCP[:,0], self.transformedCP[:,1], self.transformedCP[:,2], edgecolors='k', c=self.contigGCs, cmap=self.colorMapGC, vmin=0.0, vmax=1.0, s=np_sqrt(self.contigLengths), marker='.')
sc.set_edgecolors = sc.set_facecolors = lambda *args:None # disable depth transparency effect
self.plotStoitNames(ax1)
try:
plt.show()
plt.close(fig)
except:
print "Error showing image", exc_info()[0]
raise
del fig
def EyelinkGetGaze(targetLoc, FixLen, dispsize, el=pylink.getEYELINK(),
isET=True, PixPerDeg=None, IgnoreBlinks=False,
OversamplingBehavior=None):
""" Online gaze position output and gaze control for Eyelink 1000+.
**Author** : Wanja Mössing, WWU Münster | [email protected] \n
*July 2017*
Parameters
----------
targetLoc : tuple
two-item tuple x & y coordinates in px; defines where subject should
look at ([0, 0] is center - just as psychopy assumes it.)
FixLen : int
A circle around a specified point is set as area that subjects are
allowed to look at. ``FixLen`` defines the radius of that circle.
Can be in degree or pixels. If ``PixPerDeg`` is not empty, assumes
degree, else pixels.
el: Eyelink object
...as returned by, e.g., ``EyelinkStart()``. You can try to run it
without passing ``el``. In that case ``EyelinkGetGaze()`` will try to
find ``el``.
dispsize : tuple
Needed because PP thinks (0,0)=center, but EL thinks (0,0)= topleft
isET: boolean, default=True
Is Eyetracker connected? If ``False``, returns display center as
coordinates and ``hsmvd=False``.
PixPerDeg: float
How many pixels per one degree of visual angle? If provided, ``FixLen``
is assumed to be in degree.
IgnoreBlinks: boolean, default=False
If True, missing gaze position is replaced by center coordinates.
OversamplingBehavior: None
Defines what is returned if nothing new is available.
Returns
-------
GazeInfo: dict
Dict with elements ``x``,``y``, and ``hsmvd``. ``x`` & ``y`` are gaze
coordinates in pixels. ``hsmvd`` is boolean and defines whether gaze
left the circle set with FixLen.
"""
# IF EYETRACKER IS CONNECTED...
if isET:
# This is just for clarity
RIGHT_EYE = 1
LEFT_EYE = 0
BINOCULAR = 2
# ---------- deprecated but maybe useful later...----------------------
# check if new data available via link
# eventType = el.getNextData()
# if it's a saccade or fixation, get newest gaze data available
# if eventType in {pylink.STARTFIX, pylink.FIXUPDATE, pylink.ENDFIX,
# pylink.STARTSACC, pylink.ENDSACC}:
# if it's a blink, adjust output accordingly
# elif eventType in {pylink.STARTBLINK,pylink.ENDBLINK}:
# ---------------------------------------------------------------------
# Get the newest data sample
sample = el.getNewestSample()
# get the newest event
event = el.getNextData()
# returns none, if no new sample available
if sample is not None:
# check which eye has been tracked and retrieve data for this eye
if el.eyeAvailable() is LEFT_EYE and sample.isLeftSample():
# getGaze() return Two-item tuple in the format of
# (float, float). -> (x,y) in px
# getPupilSize return float in arbitrary units. The meaning of
# this depends on the settings made (area or diameter)
gaze = sample.getLeftEye().getGaze()
pupil = sample.getLeftEye().getPupilSize()
elif el.eyeAvailable() is RIGHT_EYE and sample.isRightSample():
gaze = sample.getRightEye().getGaze()
pupil = sample.getRightEye().getPupilSize()
elif el.eyeAvailable() is BINOCULAR:
print 'Binocular mode not yet implemented'
return None
else:
raise Exception('Could not detect which eye has been tracked')
# Check if subject blinks or if data are just randomly missing
if pylink.MISSING_DATA in gaze:
# check how sure we are whether it is a blink
if event is pylink.STARTBLINK:
print('Subject is definitely blinking') # debugging
blinked = True
elif pupil == 0:
print('Subject is probably blinking') # debugging
blinked = True
else:
print('Missing data but no blink?') # debugging
blinked = False
# assign values accordingly
if blinked and not IgnoreBlinks:
hsmvd = True
elif blinked and IgnoreBlinks:
hsmvd = False
gaze = targetLoc
elif not blinked:
hsmvd = True
else:
# Eyelink thinks (0,0) = topleft, PsyPy thinks it's center...
xhalf = dispsize[0]/2
yhalf = dispsize[1]/2
x = gaze[0]
y = gaze[1]
if x >= xhalf:
xout = x - xhalf
else:
xout = (xhalf - x) * -1
if y >= yhalf:
yout = (y - yhalf) * -1
else:
yout = yhalf - y
gaze = (xout, yout)
# transform location data to numpy arrays, so we can calculate
# euclidean distance
a = np_array(targetLoc)
b = np_array(gaze)
# get euclidean distance in px
dist = np_sqrt(np_sum((a-b)**2))
# check if we know how many px form one degree.
# If we do, convert to degree
if PixPerDeg is not None:
dist = dist/PixPerDeg
# Now check whether gaze is in allowed frame
hsmvd = dist > FixLen
# return dict
return {'x': gaze[0], 'y': gaze[1], 'hsmvd': hsmvd,
'pupilSize': pupil}
# If no new sample is available return None
elif sample is None:
return OversamplingBehavior
# IF EYETRACKER NOT CONNECTED RETURN TARGETLOCATION AND NO PUPIL SIZE
elif not isET:
return {'x': targetLoc[0], 'y': targetLoc[1], 'hsmvd': False,
'pupilSize': None}
def one2one_matches(known_complex_nodes_list, fin_list_graphs, N_pred_comp,
N_test_comp, out_comp_nm, suffix, dir_nm):
Metric = np_zeros((N_test_comp, N_pred_comp))
Common_nodes = np_zeros((N_test_comp, N_pred_comp))
known_comp_lens = np_zeros((N_test_comp, 1))
pred_comp_lens = np_zeros((1, N_pred_comp))
fl = 1
for i, test_complex in enumerate(known_complex_nodes_list):
T = set(test_complex)
known_comp_lens[i, 0] = len(T)
for j, pred_complex in enumerate(fin_list_graphs):
P = pred_complex[0]
F1_score, C = f1_similarity(P, T)
Common_nodes[i, j] = C
Metric[i, j] = F1_score
if fl == 1:
pred_comp_lens[0, j] = len(P)
fl = 0
max_indices_i_common = np_argmax(Common_nodes, axis=0)
ppv_list = [
float(Common_nodes[i, j]) / pred_comp_lens[0, j]
for j, i in enumerate(max_indices_i_common)
]
PPV = sum(ppv_list) / len(ppv_list)
max_indices_j_common = np_argmax(Common_nodes, axis=1)
sn_list = [
float(Common_nodes[i, j]) / known_comp_lens[i, 0]
for i, j in enumerate(max_indices_j_common)
]
Sn = sum(sn_list) / len(sn_list)
acc_unbiased = np_sqrt(PPV * Sn)
max_indices_i = np_argmax(Metric, axis=0)
best_matches_4predicted = [
(fin_list_graphs[j][0], known_complex_nodes_list[i], Metric[i, j],
fin_list_graphs[j][1]) for j, i in enumerate(max_indices_i)
]
max_indices_j = np_argmax(Metric, axis=1)
best_matches_4known = [(fin_list_graphs[j][0], known_complex_nodes_list[i],
Metric[i, j], fin_list_graphs[j][1])
for i, j in enumerate(max_indices_j)]
avged_f1_score4known = plot_f1_scores(
best_matches_4known, out_comp_nm, '_best4known' + suffix,
'Best predicted match for known complexes - ')
avged_f1_score4pred = plot_f1_scores(
best_matches_4predicted, out_comp_nm, '_best4predicted' + suffix,
'Best known match for predicted complexes - ')
avg_f1_score = (avged_f1_score4known + avged_f1_score4pred) / 2
net_f1_score = 2 * avged_f1_score4known * avged_f1_score4pred / (
avged_f1_score4known + avged_f1_score4pred)
write_best_matches(best_matches_4known, out_comp_nm, dir_nm,
'_best4known' + suffix)
write_best_matches(best_matches_4predicted, out_comp_nm, dir_nm,
'_best4predicted' + suffix)
prec_MMR, recall_MMR, f1_MMR, max_matching_edges = f1_mmr(Metric)
plot_pr_curve_mmr(Metric, fin_list_graphs, out_comp_nm + suffix)
n_matches = int(len(max_matching_edges) / 2)
return avg_f1_score, net_f1_score, PPV, Sn, acc_unbiased, prec_MMR, recall_MMR, f1_MMR, n_matches
def FluxRate(pressure,M=117.16,T=300):
dimerArea_in_cm2 = 0.52918**2*1e-14
torr2pa = 133.322368
from numpy import sqrt as np_sqrt
return (2.63e20*pressure*torr2pa/np_sqrt(M*T))*dimerArea_in_cm2